Field Evaluations: Proving It Works Well Enough to Trust

Pick the Metric the Business Actually Cares About

It's tempting to report the metric that's easiest to compute — token-level accuracy, embedding similarity, a generic benchmark score. None of these answer the question a stakeholder is actually asking, which is closer to "if we turn this on, does our business get better or worse, and by how much?" The right metric traces directly back to the baseline established in discovery (Article 3): resolution time, error rate, cost per case, first-contact resolution — whatever number the customer already tracks and cares about.

When no clean business metric exists yet, it's worth the extra week to define one before running the evaluation, rather than substituting a proxy metric nobody outside the engineering team understands or trusts.

Human-in-the-Loop Grading Done Right

For most enterprise tasks, correctness is a judgment call that requires domain expertise an FDE doesn't have — a claims adjuster knows whether a claims summary is accurate in a way a software engineer cannot verify independently. This makes the customer's own domain experts the right graders, not the FDE and not, on its own, an LLM-as-judge setup, however convenient that would be.

• Recruit two or three graders, not one — single-grader evaluations hide disagreement that itself is useful signal about how subjective the task is.
• Give graders a clear rubric before they start, with examples of what counts as fully correct, partially correct, and wrong — vague instructions produce inconsistent grades that undermine the whole exercise.
• Measure inter-grader agreement — if your own domain experts disagree with each other 30% of the time, that's a critical, honest data point about the task's inherent ambiguity, and it should be reported alongside the model's score, not hidden.
• Blind the grading where possible — graders shouldn't know which outputs came from the AI system and which are a human baseline, or their judgment skews in predictable directions.

Rubric-Based Grading for Open-Ended Tasks

Not every task has a single right answer. Drafting, summarizing, and brainstorming tasks are better served by a rubric that scores several independent dimensions — completeness, tone, factual grounding, adherence to format — rather than forcing a binary correct/incorrect judgment that doesn't fit the task. A rubric also produces far more actionable feedback: "strong on completeness, weak on tone" tells the team exactly what to fix, where a single pass/fail score does not.

Comparing Against the Right Baseline

The comparison that matters is rarely "AI vs. perfect," it's "AI-assisted vs. today's actual process," which usually has its own error rate that nobody has bothered to measure until now. Running the same evaluation set through the current human process — or through historical outcomes, if a live comparison isn't feasible — often produces the single most persuasive number in the whole engagement: not that the AI is flawless, but that it's measurably better than, or comparable to, what's happening today, at a fraction of the time or cost.

Communicating Uncertainty Honestly

The evaluation's credibility depends on resisting the urge to oversell it. State the sample size plainly — a 95% pass rate on 20 examples is a very different claim from 95% on 2,000, and a stakeholder with any statistical literacy will ask. Disclose what the evaluation set does and doesn't cover; if multilingual queries weren't tested, say so rather than let silence imply they were fine. And separate "the system was right in this evaluation" from "the system will always be right" — every enterprise AI deployment needs a plan for the errors that will happen after launch, and pretending otherwise sets up a credibility crisis the first time reality intrudes.

From Evaluation to Rollout Decision

A good field evaluation ends with a specific, actionable recommendation, not just a report: full rollout, staged rollout to a subset of users or cases, continued pilot with a named list of blocking issues, or — sometimes the right call — a recommendation not to proceed as scoped. Framing the evaluation as a decision-support tool, not a pass/fail exam the AI system needs to survive, keeps the conversation honest and keeps the FDE's credibility intact for the next evaluation.

Frequently Asked Questions

Is a single accuracy percentage enough to report to stakeholders?
Rarely. A single number hides which failure modes remain and how severe they are. Pair the headline metric with a breakdown by category and severity, so stakeholders can judge whether the remaining failures matter for their specific process, not just how big the number is.

Who should grade whether an AI output is correct — the FDE or the customer?
The customer's domain experts, whenever the task requires domain judgment, which is most enterprise tasks. An FDE grading their own system's output is a conflict of interest stakeholders will rightly question. Build the grading workflow around the customer's people from the start.

How do you evaluate a task that has no single correct answer?
Switch from correctness grading to a rubric — criteria like completeness, tone, and factual grounding, each scored independently, rather than a binary right or wrong. This is standard practice for open-ended tasks like drafting or summarization and produces far more actionable feedback than forcing a false binary judgment.